Overview

Brought to you by YData

Dataset statistics

Number of variables11
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory366.2 KiB
Average record size in memory375.0 B

Variable types

Categorical5
Numeric6

Alerts

average is highly overall correlated with average score and 4 other fieldsHigh correlation
average score is highly overall correlated with average and 4 other fieldsHigh correlation
math score is highly overall correlated with average and 4 other fieldsHigh correlation
reading score is highly overall correlated with average and 4 other fieldsHigh correlation
total score is highly overall correlated with average and 4 other fieldsHigh correlation
writing score is highly overall correlated with average and 4 other fieldsHigh correlation

Reproduction

Analysis started2025-08-17 10:40:17.056487
Analysis finished2025-08-17 10:40:23.048591
Duration5.99 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

gender
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size60.7 KiB
female
518 
male
482 

Length

Max length6
Median length6
Mean length5.036
Min length4

Characters and Unicode

Total characters5036
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfemale
2nd rowfemale
3rd rowfemale
4th rowmale
5th rowmale

Common Values

ValueCountFrequency (%)
female 518
51.8%
male 482
48.2%

Length

2025-08-17T11:40:23.183864image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-17T11:40:23.317109image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
female 518
51.8%
male 482
48.2%

Most occurring characters

ValueCountFrequency (%)
e 1518
30.1%
m 1000
19.9%
a 1000
19.9%
l 1000
19.9%
f 518
 
10.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5036
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1518
30.1%
m 1000
19.9%
a 1000
19.9%
l 1000
19.9%
f 518
 
10.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5036
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1518
30.1%
m 1000
19.9%
a 1000
19.9%
l 1000
19.9%
f 518
 
10.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5036
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1518
30.1%
m 1000
19.9%
a 1000
19.9%
l 1000
19.9%
f 518
 
10.3%

race/ethnicity
Categorical

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size62.6 KiB
group C
319 
group D
262 
group B
190 
group E
140 
group A
89 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters7000
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowgroup B
2nd rowgroup C
3rd rowgroup B
4th rowgroup A
5th rowgroup C

Common Values

ValueCountFrequency (%)
group C 319
31.9%
group D 262
26.2%
group B 190
19.0%
group E 140
14.0%
group A 89
 
8.9%

Length

2025-08-17T11:40:23.454700image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-17T11:40:23.582032image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
group 1000
50.0%
c 319
 
16.0%
d 262
 
13.1%
b 190
 
9.5%
e 140
 
7.0%
a 89
 
4.5%

Most occurring characters

ValueCountFrequency (%)
g 1000
14.3%
r 1000
14.3%
o 1000
14.3%
u 1000
14.3%
p 1000
14.3%
1000
14.3%
C 319
 
4.6%
D 262
 
3.7%
B 190
 
2.7%
E 140
 
2.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
g 1000
14.3%
r 1000
14.3%
o 1000
14.3%
u 1000
14.3%
p 1000
14.3%
1000
14.3%
C 319
 
4.6%
D 262
 
3.7%
B 190
 
2.7%
E 140
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
g 1000
14.3%
r 1000
14.3%
o 1000
14.3%
u 1000
14.3%
p 1000
14.3%
1000
14.3%
C 319
 
4.6%
D 262
 
3.7%
B 190
 
2.7%
E 140
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
g 1000
14.3%
r 1000
14.3%
o 1000
14.3%
u 1000
14.3%
p 1000
14.3%
1000
14.3%
C 319
 
4.6%
D 262
 
3.7%
B 190
 
2.7%
E 140
 
2.0%
Distinct6
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size70.1 KiB
some college
226 
associate's degree
222 
high school
196 
some high school
179 
bachelor's degree
118 

Length

Max length18
Median length16
Mean length14.619
Min length11

Characters and Unicode

Total characters14619
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowbachelor's degree
2nd rowsome college
3rd rowmaster's degree
4th rowassociate's degree
5th rowsome college

Common Values

ValueCountFrequency (%)
some college 226
22.6%
associate's degree 222
22.2%
high school 196
19.6%
some high school 179
17.9%
bachelor's degree 118
11.8%
master's degree 59
 
5.9%

Length

2025-08-17T11:40:23.751591image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-17T11:40:23.881918image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
some 405
18.6%
degree 399
18.3%
high 375
17.2%
school 375
17.2%
college 226
10.4%
associate's 222
10.2%
bachelor's 118
 
5.4%
master's 59
 
2.7%

Most occurring characters

ValueCountFrequency (%)
e 2453
16.8%
o 1721
11.8%
s 1682
11.5%
h 1243
8.5%
1179
8.1%
g 1000
6.8%
l 945
 
6.5%
c 941
 
6.4%
a 621
 
4.2%
i 597
 
4.1%
Other values (6) 2237
15.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14619
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2453
16.8%
o 1721
11.8%
s 1682
11.5%
h 1243
8.5%
1179
8.1%
g 1000
6.8%
l 945
 
6.5%
c 941
 
6.4%
a 621
 
4.2%
i 597
 
4.1%
Other values (6) 2237
15.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14619
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2453
16.8%
o 1721
11.8%
s 1682
11.5%
h 1243
8.5%
1179
8.1%
g 1000
6.8%
l 945
 
6.5%
c 941
 
6.4%
a 621
 
4.2%
i 597
 
4.1%
Other values (6) 2237
15.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14619
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2453
16.8%
o 1721
11.8%
s 1682
11.5%
h 1243
8.5%
1179
8.1%
g 1000
6.8%
l 945
 
6.5%
c 941
 
6.4%
a 621
 
4.2%
i 597
 
4.1%
Other values (6) 2237
15.3%

lunch
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size65.0 KiB
standard
645 
free/reduced
355 

Length

Max length12
Median length8
Mean length9.42
Min length8

Characters and Unicode

Total characters9420
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowstandard
2nd rowstandard
3rd rowstandard
4th rowfree/reduced
5th rowstandard

Common Values

ValueCountFrequency (%)
standard 645
64.5%
free/reduced 355
35.5%

Length

2025-08-17T11:40:24.051583image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-17T11:40:24.200146image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
standard 645
64.5%
free/reduced 355
35.5%

Most occurring characters

ValueCountFrequency (%)
d 2000
21.2%
e 1420
15.1%
r 1355
14.4%
a 1290
13.7%
s 645
 
6.8%
t 645
 
6.8%
n 645
 
6.8%
f 355
 
3.8%
/ 355
 
3.8%
u 355
 
3.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9420
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 2000
21.2%
e 1420
15.1%
r 1355
14.4%
a 1290
13.7%
s 645
 
6.8%
t 645
 
6.8%
n 645
 
6.8%
f 355
 
3.8%
/ 355
 
3.8%
u 355
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9420
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 2000
21.2%
e 1420
15.1%
r 1355
14.4%
a 1290
13.7%
s 645
 
6.8%
t 645
 
6.8%
n 645
 
6.8%
f 355
 
3.8%
/ 355
 
3.8%
u 355
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9420
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 2000
21.2%
e 1420
15.1%
r 1355
14.4%
a 1290
13.7%
s 645
 
6.8%
t 645
 
6.8%
n 645
 
6.8%
f 355
 
3.8%
/ 355
 
3.8%
u 355
 
3.8%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size61.4 KiB
none
642 
completed
358 

Length

Max length9
Median length4
Mean length5.79
Min length4

Characters and Unicode

Total characters5790
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownone
2nd rowcompleted
3rd rownone
4th rownone
5th rownone

Common Values

ValueCountFrequency (%)
none 642
64.2%
completed 358
35.8%

Length

2025-08-17T11:40:24.354413image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-17T11:40:24.469937image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
none 642
64.2%
completed 358
35.8%

Most occurring characters

ValueCountFrequency (%)
e 1358
23.5%
n 1284
22.2%
o 1000
17.3%
c 358
 
6.2%
m 358
 
6.2%
p 358
 
6.2%
l 358
 
6.2%
t 358
 
6.2%
d 358
 
6.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5790
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1358
23.5%
n 1284
22.2%
o 1000
17.3%
c 358
 
6.2%
m 358
 
6.2%
p 358
 
6.2%
l 358
 
6.2%
t 358
 
6.2%
d 358
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5790
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1358
23.5%
n 1284
22.2%
o 1000
17.3%
c 358
 
6.2%
m 358
 
6.2%
p 358
 
6.2%
l 358
 
6.2%
t 358
 
6.2%
d 358
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5790
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1358
23.5%
n 1284
22.2%
o 1000
17.3%
c 358
 
6.2%
m 358
 
6.2%
p 358
 
6.2%
l 358
 
6.2%
t 358
 
6.2%
d 358
 
6.2%

math score
Real number (ℝ)

High correlation 

Distinct81
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.089
Minimum0
Maximum100
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-08-17T11:40:24.633204image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile40.95
Q157
median66
Q377
95-th percentile90.05
Maximum100
Range100
Interquartile range (IQR)20

Descriptive statistics

Standard deviation15.16308
Coefficient of variation (CV)0.22943425
Kurtosis0.27496406
Mean66.089
Median Absolute Deviation (MAD)10
Skewness-0.27893515
Sum66089
Variance229.919
MonotonicityNot monotonic
2025-08-17T11:40:24.817101image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65 36
 
3.6%
62 35
 
3.5%
69 32
 
3.2%
59 32
 
3.2%
61 27
 
2.7%
73 27
 
2.7%
63 26
 
2.6%
67 26
 
2.6%
68 26
 
2.6%
71 26
 
2.6%
Other values (71) 707
70.7%
ValueCountFrequency (%)
0 1
0.1%
8 1
0.1%
18 1
0.1%
19 1
0.1%
22 1
0.1%
23 1
0.1%
24 1
0.1%
26 1
0.1%
27 2
0.2%
28 1
0.1%
ValueCountFrequency (%)
100 7
0.7%
99 3
 
0.3%
98 3
 
0.3%
97 6
0.6%
96 3
 
0.3%
95 2
 
0.2%
94 7
0.7%
93 4
0.4%
92 6
0.6%
91 9
0.9%

reading score
Real number (ℝ)

High correlation 

Distinct72
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.169
Minimum17
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-08-17T11:40:24.986981image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile44
Q159
median70
Q379
95-th percentile92
Maximum100
Range83
Interquartile range (IQR)20

Descriptive statistics

Standard deviation14.600192
Coefficient of variation (CV)0.21107999
Kurtosis-0.068265459
Mean69.169
Median Absolute Deviation (MAD)10
Skewness-0.25910452
Sum69169
Variance213.1656
MonotonicityNot monotonic
2025-08-17T11:40:25.167077image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72 34
 
3.4%
74 33
 
3.3%
64 32
 
3.2%
67 30
 
3.0%
73 30
 
3.0%
58 28
 
2.8%
78 27
 
2.7%
66 27
 
2.7%
75 26
 
2.6%
70 26
 
2.6%
Other values (62) 707
70.7%
ValueCountFrequency (%)
17 1
 
0.1%
23 1
 
0.1%
24 2
0.2%
26 1
 
0.1%
28 1
 
0.1%
29 2
0.2%
31 2
0.2%
32 1
 
0.1%
34 4
0.4%
37 3
0.3%
ValueCountFrequency (%)
100 17
1.7%
99 3
 
0.3%
97 5
 
0.5%
96 4
 
0.4%
95 8
0.8%
94 3
 
0.3%
93 6
 
0.6%
92 10
1.0%
91 6
 
0.6%
90 17
1.7%

writing score
Real number (ℝ)

High correlation 

Distinct77
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.054
Minimum10
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-08-17T11:40:25.350511image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile42.95
Q157.75
median69
Q379
95-th percentile92
Maximum100
Range90
Interquartile range (IQR)21.25

Descriptive statistics

Standard deviation15.195657
Coefficient of variation (CV)0.22328823
Kurtosis-0.033364615
Mean68.054
Median Absolute Deviation (MAD)11
Skewness-0.28944397
Sum68054
Variance230.90799
MonotonicityNot monotonic
2025-08-17T11:40:25.587064image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
74 35
 
3.5%
70 33
 
3.3%
68 31
 
3.1%
73 28
 
2.8%
80 27
 
2.7%
62 27
 
2.7%
67 25
 
2.5%
72 25
 
2.5%
54 25
 
2.5%
76 25
 
2.5%
Other values (67) 719
71.9%
ValueCountFrequency (%)
10 1
 
0.1%
15 1
 
0.1%
19 1
 
0.1%
22 1
 
0.1%
23 1
 
0.1%
27 3
0.3%
28 1
 
0.1%
30 1
 
0.1%
32 2
0.2%
33 2
0.2%
ValueCountFrequency (%)
100 14
1.4%
99 4
 
0.4%
98 2
 
0.2%
97 2
 
0.2%
96 4
 
0.4%
95 8
0.8%
94 6
0.6%
93 8
0.8%
92 9
0.9%
91 11
1.1%

total score
Real number (ℝ)

High correlation 

Distinct194
Distinct (%)19.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean203.312
Minimum27
Maximum300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-08-17T11:40:25.849827image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile133.95
Q1175
median205
Q3233
95-th percentile270.05
Maximum300
Range273
Interquartile range (IQR)58

Descriptive statistics

Standard deviation42.771978
Coefficient of variation (CV)0.21037606
Kurtosis0.12584287
Mean203.312
Median Absolute Deviation (MAD)29
Skewness-0.29905712
Sum203312
Variance1829.4421
MonotonicityNot monotonic
2025-08-17T11:40:26.024474image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
204 15
 
1.5%
198 14
 
1.4%
232 14
 
1.4%
205 13
 
1.3%
210 12
 
1.2%
223 12
 
1.2%
207 12
 
1.2%
214 12
 
1.2%
206 12
 
1.2%
219 12
 
1.2%
Other values (184) 872
87.2%
ValueCountFrequency (%)
27 1
0.1%
55 1
0.1%
69 1
0.1%
70 1
0.1%
78 2
0.2%
88 1
0.1%
89 2
0.2%
90 1
0.1%
92 1
0.1%
93 1
0.1%
ValueCountFrequency (%)
300 3
0.3%
299 1
 
0.1%
297 2
0.2%
296 2
0.2%
293 3
0.3%
292 2
0.2%
291 2
0.2%
290 1
 
0.1%
289 3
0.3%
288 1
 
0.1%

average score
Real number (ℝ)

High correlation 

Distinct194
Distinct (%)19.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.770667
Minimum9
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-08-17T11:40:26.186948image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile44.65
Q158.333333
median68.333333
Q377.666667
95-th percentile90.016667
Maximum100
Range91
Interquartile range (IQR)19.333333

Descriptive statistics

Standard deviation14.257326
Coefficient of variation (CV)0.21037606
Kurtosis0.12584287
Mean67.770667
Median Absolute Deviation (MAD)9.6666667
Skewness-0.29905712
Sum67770.667
Variance203.27134
MonotonicityNot monotonic
2025-08-17T11:40:26.373034image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68 15
 
1.5%
66 14
 
1.4%
77.33333333 14
 
1.4%
68.33333333 13
 
1.3%
70 12
 
1.2%
74.33333333 12
 
1.2%
69 12
 
1.2%
71.33333333 12
 
1.2%
68.66666667 12
 
1.2%
73 12
 
1.2%
Other values (184) 872
87.2%
ValueCountFrequency (%)
9 1
0.1%
18.33333333 1
0.1%
23 1
0.1%
23.33333333 1
0.1%
26 2
0.2%
29.33333333 1
0.1%
29.66666667 2
0.2%
30 1
0.1%
30.66666667 1
0.1%
31 1
0.1%
ValueCountFrequency (%)
100 3
0.3%
99.66666667 1
 
0.1%
99 2
0.2%
98.66666667 2
0.2%
97.66666667 3
0.3%
97.33333333 2
0.2%
97 2
0.2%
96.66666667 1
 
0.1%
96.33333333 3
0.3%
96 1
 
0.1%

average
Real number (ℝ)

High correlation 

Distinct194
Distinct (%)19.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.770667
Minimum9
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-08-17T11:40:26.596268image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile44.65
Q158.333333
median68.333333
Q377.666667
95-th percentile90.016667
Maximum100
Range91
Interquartile range (IQR)19.333333

Descriptive statistics

Standard deviation14.257326
Coefficient of variation (CV)0.21037606
Kurtosis0.12584287
Mean67.770667
Median Absolute Deviation (MAD)9.6666667
Skewness-0.29905712
Sum67770.667
Variance203.27134
MonotonicityNot monotonic
2025-08-17T11:40:26.787815image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68 15
 
1.5%
66 14
 
1.4%
77.33333333 14
 
1.4%
68.33333333 13
 
1.3%
70 12
 
1.2%
74.33333333 12
 
1.2%
69 12
 
1.2%
71.33333333 12
 
1.2%
68.66666667 12
 
1.2%
73 12
 
1.2%
Other values (184) 872
87.2%
ValueCountFrequency (%)
9 1
0.1%
18.33333333 1
0.1%
23 1
0.1%
23.33333333 1
0.1%
26 2
0.2%
29.33333333 1
0.1%
29.66666667 2
0.2%
30 1
0.1%
30.66666667 1
0.1%
31 1
0.1%
ValueCountFrequency (%)
100 3
0.3%
99.66666667 1
 
0.1%
99 2
0.2%
98.66666667 2
0.2%
97.66666667 3
0.3%
97.33333333 2
0.2%
97 2
0.2%
96.66666667 1
 
0.1%
96.33333333 3
0.3%
96 1
 
0.1%

Interactions

2025-08-17T11:40:21.682239image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-17T11:40:17.659442image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-17T11:40:18.514409image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-17T11:40:19.296353image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-17T11:40:20.052025image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-17T11:40:20.954357image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-17T11:40:21.798579image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-17T11:40:17.833676image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-17T11:40:18.650684image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-17T11:40:19.415380image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-17T11:40:20.182970image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-17T11:40:21.071692image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-17T11:40:21.918182image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-17T11:40:17.958963image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-17T11:40:18.757853image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-17T11:40:19.532067image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-17T11:40:20.319427image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-17T11:40:21.185444image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-17T11:40:22.055729image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-17T11:40:18.085069image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-17T11:40:18.901056image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-17T11:40:19.648147image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-17T11:40:20.485592image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-17T11:40:21.317770image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-17T11:40:22.174685image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-17T11:40:18.242158image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-17T11:40:19.031855image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-17T11:40:19.782301image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-17T11:40:20.625808image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-17T11:40:21.443524image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-17T11:40:22.290931image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-17T11:40:18.380683image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-17T11:40:19.169394image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-17T11:40:19.927202image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-17T11:40:20.782350image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-08-17T11:40:21.563953image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Correlations

2025-08-17T11:40:26.903796image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
averageaverage scoregenderlunchmath scoreparental level of educationrace/ethnicityreading scoretest preparation coursetotal scorewriting score
average1.0001.0000.1390.2770.9090.0810.0780.9690.2551.0000.959
average score1.0001.0000.1390.2770.9090.0810.0780.9690.2551.0000.959
gender0.1390.1391.0000.0000.1430.0000.0710.2440.0000.1390.301
lunch0.2770.2770.0001.0000.3560.0000.0000.2200.0000.2770.234
math score0.9090.9090.1430.3561.0000.0610.1220.8040.1550.9090.778
parental level of education0.0810.0810.0000.0000.0611.0000.0490.0770.0670.0810.098
race/ethnicity0.0780.0780.0710.0000.1220.0491.0000.0490.0390.0780.071
reading score0.9690.9690.2440.2200.8040.0770.0491.0000.2230.9690.949
test preparation course0.2550.2550.0000.0000.1550.0670.0390.2231.0000.2550.301
total score1.0001.0000.1390.2770.9090.0810.0780.9690.2551.0000.959
writing score0.9590.9590.3010.2340.7780.0980.0710.9490.3010.9591.000

Missing values

2025-08-17T11:40:22.723755image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
A simple visualization of nullity by column.
2025-08-17T11:40:22.951520image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

genderrace/ethnicityparental level of educationlunchtest preparation coursemath scorereading scorewriting scoretotal scoreaverage scoreaverage
0femalegroup Bbachelor's degreestandardnone72727421872.66666772.666667
1femalegroup Csome collegestandardcompleted69908824782.33333382.333333
2femalegroup Bmaster's degreestandardnone90959327892.66666792.666667
3malegroup Aassociate's degreefree/reducednone47574414849.33333349.333333
4malegroup Csome collegestandardnone76787522976.33333376.333333
5femalegroup Bassociate's degreestandardnone71837823277.33333377.333333
6femalegroup Bsome collegestandardcompleted88959227591.66666791.666667
7malegroup Bsome collegefree/reducednone40433912240.66666740.666667
8malegroup Dhigh schoolfree/reducedcompleted64646719565.00000065.000000
9femalegroup Bhigh schoolfree/reducednone38605014849.33333349.333333
genderrace/ethnicityparental level of educationlunchtest preparation coursemath scorereading scorewriting scoretotal scoreaverage scoreaverage
990malegroup Ehigh schoolfree/reducedcompleted86817524280.66666780.666667
991femalegroup Bsome high schoolstandardcompleted65827822575.00000075.000000
992femalegroup Dassociate's degreefree/reducednone55767620769.00000069.000000
993femalegroup Dbachelor's degreefree/reducednone62727420869.33333369.333333
994malegroup Ahigh schoolstandardnone63636218862.66666762.666667
995femalegroup Emaster's degreestandardcompleted88999528294.00000094.000000
996malegroup Chigh schoolfree/reducednone62555517257.33333357.333333
997femalegroup Chigh schoolfree/reducedcompleted59716519565.00000065.000000
998femalegroup Dsome collegestandardcompleted68787722374.33333374.333333
999femalegroup Dsome collegefree/reducednone77868624983.00000083.000000